Demand-Aware Network Design with Minimal Congestion and Route Lengths

Chen Avin, Kaushik Mondal, Stefan Schmid

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    25 Scopus citations

    Abstract

    Emerging communication technologies allow to reconfigure the physical network topology at runtime, enabling demand-aware networks (DANs): networks whose topology is optimized toward the workload they serve. However, today, only little is known about the fundamental algorithmic problems underlying the design of such demand-aware networks. This paper presents the first bounded-degree, demand-aware network, ct-DAN, which minimizes both congestion and route lengths. The designed network is provably (asymptotically) optimal in each dimension individually: we show that there do not exist any bounded-degree networks providing shorter routes (independently of the load), nor do there exist networks providing lower loads (independently of the route lengths). The main building block of the designed ct-DAN networks are ego-trees: communication sources arrange their communication partners in an optimal tree, individually. While the union of these ego-trees forms the basic structure of cl-DANs, further techniques are presented to ensure bounded degrees (for scalability).

    Original languageEnglish
    Title of host publicationINFOCOM 2019 - IEEE Conference on Computer Communications
    PublisherInstitute of Electrical and Electronics Engineers
    Pages1351-1359
    Number of pages9
    ISBN (Electronic)9781728105154
    DOIs
    StatePublished - 1 Apr 2019
    Event2019 IEEE Conference on Computer Communications, INFOCOM 2019 - Paris, France
    Duration: 29 Apr 20192 May 2019

    Publication series

    NameProceedings - IEEE INFOCOM
    Volume2019-April
    ISSN (Print)0743-166X

    Conference

    Conference2019 IEEE Conference on Computer Communications, INFOCOM 2019
    Country/TerritoryFrance
    CityParis
    Period29/04/192/05/19

    ASJC Scopus subject areas

    • General Computer Science
    • Electrical and Electronic Engineering

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